Volume 50 Issue 5, May 2018

The ability to visualize and study the 3D folding of chromosomes in cells has been propelled forward by several major technological advances in the past two decades. Two new studies now further expand the scientific toolbox for studying chromosome conformation by providing novel methodologies for accurate mapping of genome topology and predicting the topological effects of genomic structural variation.

The triplet code underpins analyses of rare and de novo mutations in exome sequencing data, but analysis of the noncoding genome is much more challenging. A new analytical framework for common, complex diseases highlights the need for very large samples to overcome the unavoidable multiple-testing burden and hence provides preemptive warnings against underpowered studies.

This study provides genome-wide evidence of PRC2 recruitment by telomere-repeat-binding factors (TRBs) through telobox-related motifs in Arabidopsis. Telobox-related motifs recruit PRC2 through the interaction between TRBs and CLF/SWN.

Meta-analysis of exome sequencing data identifies new recurrently mutated driver genes for prostate cancer. Comparison of primary and metastatic tumors further identifies genomic markers for advanced prostate cancer that may inform risk stratification.

Genome-wide meta-analysis identifies >100 loci associated with hair color variation in humans of European ancestry. These loci explain a large portion of the heritability of this trait & provide insights into pathways regulating hair pigmentation.

This study presents evidence that siRNAs or miRNAs with seed sequences that overlap RBP motifs have extended biological effects by perturbing RBP activity. Seed-to-RBP crosstalk contributes to off-target activity and growth phenotype modulation.

The authors present a polymer-physics-based approach (PRISMR) to model 3D chromatin folding and to predict enhancer–promoter contacts. PRISMR correctly predicts ectopic contacts induced by pathogenic SVs at the mouse Epha4 locus.

A genome-wide association meta-analysis of individuals with clinically assessed or self-reported depression identifies 44 independent risk loci. Comparisons with other psychiatric disorders and candidate gene analyses provide new insights into major depressive disorder.

Joint analysis of new and previously published sequencing data for primary and metastatic prostate cancers identifies new candidate driver mutations and provides insights into disease progression and potential drug targets.

The MR-PRESSO test detects and corrects horizontal pleiotropy in multi-instrument Mendelian randomization (MR) analyses. Applying the MR-PRESSO test to 4,250 MR tests of complex traits and diseases finds horizontal pleiotropy in >48% of causal relationships.

Linking transcription factors with disease loci implicates EBNA2, encoded by Epstein–Barr virus, in autoimmune diseases. Applying the method more widely identifies associations for hundreds of transcription factors, illuminating disease mechanisms.

Analysis of glioblastoma samples and derived neurospheres and xenografts shows that chromosomal and extrachromosomal alterations often display divergent inheritance patterns during cell culture and xenografting.

MACHINA is an algorithm that analyzes metastatic cancer sequence data to simultaneously infer clone trees and migration histories. Analysis of different metastatic cancer datasets provides more evidence for simple, rather than complex, migration patterns.

This study presents a framework to evaluate rare and de novo variation from whole-genome sequencing (WGS). The work suggests that robust results from WGS studies will require large cohorts and strategies that consider the substantial multiple-testing burden.

This analysis compares methods for estimating the heritability and genetic architecture of complex traits using whole-genome data. The results provide guidance for best practices and proper interpretation of published heritability estimates.

BayesS estimates SNP-based heritability, polygenicity, and the relationship between effect size and minor allele frequency using genome-wide SNP data. Applying BayesS to UK Biobank data identifies signatures of natural selection for 23 complex traits.

DLO Hi-C is a new method to investigate the 3D genome. It requires only two rounds of digestion and ligation and removes non-ligated DNA in a cost-effective step by purifying specific linker-ligated DNA fragments.